What if AI could help reduce the 10-plus years it takes to get a new drug to market? That question has shaped much of my own product strategy thinking, and it’s exactly why I was drawn to Medable’s bold move with Agent Studio. It’s a rare look inside an enterprise AI platform built for one of the most regulated industries in the world—and a team that’s still figuring it out in real time.
In this episode of Just Now Possible, Teresa Torres talks with four members of the Medable team: Luke Bates (Product Leader, Agent Studio), Jen Brown (Product Manager), Matt Schoolfield (Product Designer), and Fiachra Matthews (Principal Architect). Listening through a product management lens, I focused on how their choices reflect a modern agentic AI strategy that balances speed, safety, and scale.
Medable does something uniquely hard: enabling global clinical trials across 100+ languages and accelerating drug-to-market timelines. That scope demands more than clever prompts—it requires a durable platform approach. Their answer is Agent Studio, a no-code/low-code platform for configuring and deploying agents across the clinical trial lifecycle.
What impressed me most was how clearly the platform’s primitives map to repeatable value: models, skills, knowledge bases, MCP connectors, versioning, and trigger types. In my experience, platforms win when these building blocks are composable, governed, and observable—exactly the direction Medable is taking.
You’ll also hear about the two agents they’ve built on top of it: an ETMF agent that automates document classification across 80,000-plus documents per year, and a CRA agent that monitors patient safety and data quality across 13 different clinical systems. For a domain where errors carry real human consequences, this is the right mix of automation and oversight.
Under the hood, their architecture choices echo what I’ve seen work in other high-stakes environments. They walk through RAG approaches at scale: embeddings vs. markdown hierarchies vs. just-in-time MCP retrieval, and explain Why they built custom MCPs with an authentication and credentialing wrapper. They also detail Context window management with sub-agents and automatic tool filtering—critical to keep agents focused and reliable as complexity grows.
Data alignment is often the unsung hero of agent reliability. I appreciated how they described How they built a unified ontology layer to map terminology across 13 different clinical data systems. Equally important, they show their paper trail: How they document agent intent → specification → test evidence to satisfy regulatory bodies. In a GXP context, this kind of lineage isn’t “nice to have”—it’s the price of admission.
Discover how Medable's Agent Studio reimagines clinical operations, shrinking drug-to-market timelines from a decade to a year with no-code agents, automated eTMF document classification, unified data monitoring, and human-in-the-loop validation.
Strategically, I love that Medable chose a platform approach to agents instead of one-off builds. They outline Three deployment models: Medable-built products, services-led custom builds, and self-serve platform access. This mirrors a healthy platform business model: prove value with first-party solutions, extend via services for complex needs, and unlock scale with self-serve—while keeping governance centralized.
Reliability is a theme throughout. They describe Evaluation design in a GXP-regulated environment: golden datasets, production monitoring, and the challenge of human feedback as ground truth. We also get a concrete picture of what human-in-the-loop really looks like when clinical decisions are on the line—tight feedback cycles, auditable interventions, and clear escalation paths.
Looking forward, they don’t shy away from ambition. The "full self-driving" vision for clinical trials and what it would take to get there is both provocative and grounded. My read: the path runs through stronger domain ontologies, standardized interfaces (MCP done right), eval-driven development, and relentless simplification of agent skills.
If you’re a product leader building in regulated spaces, this discussion is a masterclass in balancing innovation with compliance. The takeaways map cleanly to AI Strategy: define platform primitives, invest in retrieval-first pipeline patterns, design for context window management, lean into eval-driven development, and operationalize regulatory compliance from day one.
To dive deeper, listen to the conversation on Spotify or Apple Podcasts, and explore Medable’s broader platform work at medable.com. I left both inspired and practically equipped—an uncommon combo in today’s AI noise.
In my role leading product teams at HighLevel, I’m often asked to explain what’s really happening behind the scenes of today’s AI products. The short answer is that modern systems are built on "Agentic Architecture: How Modern AI Systems Actually Work"—not just a single model, but a coordinated loop of planning, tool use, memory, and evaluation. Once you see that pattern, the design decisions snap into focus and the roadmap becomes far easier to prioritize.
At its core, agentic AI treats the model as a reasoning engine embedded within an AI workflow. The agent interprets intent, plans steps, calls the right tools and APIs, grounds itself in trusted data, and then evaluates outcomes before deciding to continue or stop. This loop creates reliability, reduces hallucinations, and enables the system to operate in real-world, multi-step scenarios.
Here’s the practical lifecycle I rely on. A user provides intent (a goal or request). We run a retrieval-first pipeline to ground the model in accurate, current data. Prompt engineering structures the task and primes the agent with constraints and success criteria while managing context window management. The agent generates a plan, executes steps by calling tools or services, evaluates intermediate results, reflects or revises as needed, and only then returns a final answer with clear citations or evidence.
For more complex work, I orchestrate multiple specialized agents—commonly a planner, a solver, and a critic—coordinated by a lightweight controller. This multi-agent pattern reduces single-agent blind spots, encourages self-checking, and mirrors how empowered product teams collaborate. Whether it’s conversation design for support flows or a voice AI agent driving hands-free tasks, orchestration is the difference between a clever demo and a dependable product.
Memory is the second pillar. Short-term working context sits in the prompt, while long-term memory lives in vector stores or databases to track past interactions, preferences, and outcomes. Retrieval augments the model with the right facts at the right time, and tight context window management ensures the agent stays focused on signal, not noise. The result is faster responses, lower costs, and far better accuracy.
Reliability is earned through eval-driven development and robust AI risk management. I define offline and online evaluations, guardrails, and human-in-the-loop checkpoints before scaling traffic. These evaluations become living, automated tests that protect against regressions as prompts, models, and tools evolve. The payoff is real: fewer escalations, higher trust, and measurable improvements to quality over time.
From a product strategy perspective, I resist over-engineering. Start with a simple retrieval-first pipeline and a single agent; prove value; then layer in multi-agent orchestration only where it moves key metrics. Instrument everything—latency, cost, grounding coverage, and outcome quality—and build Agent Analytics dashboards so teams can diagnose issues and iterate with confidence.
If you’re looking for a practical playbook, here’s mine: clarify the user intent and success criteria; design the tools the agent can call; ground with authoritative data; write prompts that constrain scope and define termination conditions; add reflection and automated evaluations; and ship behind feature flags for safe, staged rollout. Each step compounds reliability without killing velocity.
The diagram and the video above bring these patterns to life. If you watch closely, you’ll see the same loop—plan, retrieve, act, evaluate—show up in every effective implementation, regardless of domain. That repetition isn’t accidental; it’s the backbone of agentic architecture and a blueprint you can adapt to your own stack.
Ultimately, what matters is outcomes. When we build around agentic AI, we create systems that are explainable to stakeholders, maintainable by engineers, and genuinely helpful to customers. That’s how we move past hype to durable impact—shipping AI products that plan, learn, and execute at scale.
Leading the Support function for a company that builds a leading Agent and AI-forward customer service platform has been, for me, unique, exciting, and yes—daunting. It’s where product ambition meets operational reality, and where every decision I make is immediately tested by customers who expect excellence.
It’s unique because we use the same technology as our customers. We live in the product every day, which puts us in a privileged position to be the voice of the customer across the organization. That tight feedback loop has shaped how I prioritize, what I build next, and how I measure success.
It’s exciting because we get to try all of the new features and capabilities of Fin and the Intercom helpdesk. With a relentless focus on AI innovation, I’ve had access to remarkable tools that help us deliver an incredible customer experience—and I’ve seen firsthand how the right workflows and guardrails turn those tools into outcomes.
And it’s daunting because expectations for our own Customer Support (CS) team are sky high. If we can’t deliver incredible support using our own technology, we undermine its value proposition. That imperative has kept me honest, focused, and fast.
In our new research, “The 2026 Customer Service Transformation Report,” we’ve been sharing how forward-looking teams use AI to transform their support models. If you’d like to get straight to the report, download it here.
When Intercom changed its focus in late 2022 to prioritize the customer service use case, we undertook a critical review of the support experience we were delivering and committed to driving meaningful change under an AI-first framework. That was a turning point: I aligned product strategy and operations around a single north star—automate with quality, and elevate humans to higher-value work.
Three years on, Fin now resolves over 81% of all our customer support volume, delivering immediate and high-quality resolutions. We have absorbed a 300%+ increase in customer demand since 2022 without proportional headcount growth. Without Fin, we would have needed at least 100 additional CS team members to meet that demand and our improved service levels – a net saving to Intercom of between $7.5M–$9M annually.
Throughout this work, we drew on research from the 2026 Customer Service Transformation Report and applied the lessons directly to our own org design, knowledge management, and AI workflows. What follows is our story of transformation and how we achieved a mature deployment of Fin.
The problems we set out to solve
Back in 2022, our challenges looked familiar to any modern support organization, and I knew we needed a step-change—not incremental tweaks.
We faced increased support demand from new and existing customers: Intercom was launching major features and changes at speed, driving up overall customer conversation volume and requiring additional headcount for the CS team. I could see we were scaling people faster than processes—unsustainable without automation.
Our support policy (as defined by our service level objectives) was not based on a high bar: In most cases, we were only committed to “business hours” coverage for the majority of our customers, impacting first response times. Even with SLOs that were not considered best in class, we were struggling to meet our commitments. I wanted 24/7 coverage and faster first responses without sacrificing quality.
We wanted to do more: As we pivoted our strategy, we wanted to open new routes to our support team, such as providing support to website visitors with technical questions and to trial customers. That meant meeting customers earlier in their journey with accurate, on-brand responses—at scale.
What we did
We made a very conscious decision to become our own best reference customer. As Intercom embraced the opportunity that generative AI presented to transform customer service, we intentionally moved to an AI-first strategy for our Customer Support team. I set a simple operating principle: ship value quickly, measure relentlessly, and let evidence guide the next bet.
We started with the highest-volume, informational queries and saw our resolution rates climb quickly. With that foundation in place, we pushed Fin further, training it on deeper documentation and internal procedures, and eventually giving it the ability to take actions on behalf of customers. As Fin took on more complex work, our results started to compound—and trust in the system grew across the organization.
Early adoption and building trust. When “AI Assist” features came to the Intercom Inbox, the CS team got early exposure to AI and were empowered to provide feedback directly to our product teams. This built awareness and trust across the team about what we were trying to achieve with AI, and helped shape the product roadmap. We were also the first beta customer for Fin, rolling it out to a subset of customers to watch sentiment and outcomes closely. With no adverse reaction and an initial resolution rate of over 25%, we deployed Fin to most customer segments within weeks. I’ll never forget the first week we put Fin in front of real customers—the silence of issues that never reached humans was the loudest signal of success.
Knowledge management as a product. We recognized quickly that time spent tuning our help center and knowledge assets for Fin would pay dividends. We transitioned our Help Center Manager into a “Knowledge Manager,” with a dedicated remit to optimize content for Fin. We embedded knowledge creation into our “New Product Introduction” (NPI) process, targeting that Fin would resolve at least 50% of customer issues at every new product and feature launch. Over time, we added new sources, including “Developer Documents,” enabling Fin to handle increasingly complex issues. We built a culture of continuous improvement—allocating “out of the inbox” time so every teammate could close content gaps and raise the bar.
Conversation design end-to-end. To ensure a consistent, high-quality customer experience, we created a new “Conversation Designer” role that owns the journey across automation and human handoffs. Using Intercom’s Workflows, we introduced “skills-based routing” so that when a customer asks for a human, the conversation reaches someone with the right expertise quickly. This is now handled by Fin directly using a feature called “Attributes.” The result: a seamless, on-brand experience regardless of channel or escalation path.
Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.
Organization changes that unlocked leverage. As we scaled Fin, we stood up a dedicated AI Support team under a senior CS leader to continuously optimize automation and define our AI adoption strategy across the journey. We restructured human roles into “Technical Support Specialist” and “Technical Support Engineer” to better align with the complexity of incoming work. We also expanded Support Operations to focus on optimization—using AI to uplevel Enablement, Workforce Management, QA, Process Management, and Data Insights. Just as important, we reset expectations about the balance between time spent supporting customers directly versus improving AI. That mindset shift created compounding returns.
Pushing Fin further with new capabilities. As capabilities matured, we were early adopters and saw measurable wins:
Fin Guidance: Multiple Guidance rules provide additional controls and a more personalized, targeted experience for customers.
Fin Tasks and Procedures: Enables Fin to carry out activities such as updating customers on incident status and deep troubleshooting for technical issues.
Insights: AI-driven dashboards provide deep insight into Fin’s performance and surface recommendations for further optimization. Insights also provides a Customer Experience (CX) Score for every customer interaction, enabling more targeted improvement efforts and opening up new ways to close the loop with customers who have had a poor experience.
What we achieved
What started as a focused effort to improve our customer support experience became the strongest proof point for what’s possible when you fully embrace AI. Fin now resolves over 81% of all our customer support volume and has allowed us to absorb a 300%+ increase in demand without proportional headcount growth. Over 90% of our customers now benefit from improved first response performance, 24/7 coverage, and outbound phone support.
What the numbers don’t fully capture is the shift in how our team operates. With volume absorbed by Fin, our CS teammates now deliver consultative support—guiding next best actions, deepening product adoption, and contributing directly to retention and expansion. Customers that receive these engagements adopt Fin at a much deeper level and achieve greater support success. What was once a reactive, volume-driven team is now a function that generates significant revenue.
What’s next
Customer expectations are always rising, so we’re building on our progress by embracing the Fin Flywheel—an actionable framework for ongoing improvement and optimization. This keeps us honest about the discipline required to sustain AI performance at scale.
Train: Teach Fin to resolve even the most complex queries with Procedures, knowledge, and policies.
Test: Run fully simulated customer conversations from start to finish to see exactly how Fin will behave before going live.
Deploy: Set Fin live across every channel – voice, email, chat, and social – for consistent support wherever customers reach out.
Analyze: Use AI-powered Insights to analyze and improve Fin’s performance and deliver better customer experiences.
We are also investing in our support teammates so they can adjust to the new world of AI—taking on more complex work and being valued for the subject matter expertise, consultative engagement, and empathy they bring to the role. That human layer is where differentiation shines.
We will continue to develop and share best practices for deploying an Agent, based on our own experience with Fin and the lessons learned from our most forward-looking customers. These are captured and continually evolving in The Agent Blueprint.
Transformation takes commitment
The most successful teams aren’t bolting AI onto old processes; they’re rebuilding support around it—investing in knowledge and people alongside technology, and treating AI as a continuous discipline rather than a one-time deployment. That’s the real change required. For support teams willing to make it, there’s a rare opportunity to redefine what customer service can deliver—higher CSAT, faster resolution, and durable ROI.
I’ve long believed a simple truth about AI in customer support: if AI is going to earn trust, pricing has to be aligned with value. That principle has guided my product decisions and the way I hold our teams accountable for measurable outcomes, not activity.
When we shared our perspective on pricing AI Agents in 2023, we made a simple argument: if AI is going to earn trust, pricing has to be aligned with value. At the time for Fin, that value was clear. You pay when the AI resolves a customer’s problem. If it doesn’t, you don’t. That’s fair, easy to understand, and grounded in results, not activity. We were the first to introduce this pricing model because we believed that pricing and value should be inherently linked.
That belief hasn’t changed, it’s grown stronger over time. What’s changed is what Fin can do. As we expanded capabilities and pushed deeper into complex workflows, it became clear that measuring value solely by end-to-end resolutions no longer captured the full picture of impact.
Resolutions were the right place to start. Historically, we measured value based on whether Fin fully resolved a conversation on its own. These are known as resolutions and they gave support teams a clear way to measure ROI, easily comparing the cost of AI versus human support. They also aligned our incentives with our customers, as our revenue was directly tied to Fin’s performance.
That clarity worked. Today, more than 7,000 teams use Fin. Our average resolution rate across customers has increased every month and now stands at 67%, even as Fin increasingly handles more complex queries. That progress came from building an Agent that could take on harder problems and still deliver.
But as Fin got more powerful, “success” stopped being binary. I saw this first-hand in customer design sessions where policy, risk, and compliance needs rightly demanded human-in-the-loop confirmation. We weren’t failing to deliver value; we were delivering it differently.
Over the last couple of years, we invested heavily to ensure Fin could handle the most complex parts of support. As Fin’s capabilities expanded, customers began pushing what Fin can do for them by deploying Fin deeper into their workflows to handle the toughest queries.
In some cases, this required Fin to work in tandem with a human agent because that’s what customer policies and oversight needs dictated. Subscription changes, transaction disputes, billing issues, and other multi-step support scenarios can often require Fin to gather context, read and write to external systems, and execute actions before handing off to a human agent for confirmation.
Fin is still doing what it was configured for – intentionally handing off after doing more of the heavy lifting, saving valuable time for support teams and overall time to serve for their customers. But our pricing metric only recognized value when the conversation ended in a full “AI resolution” (i.e. a human was never involved).
That’s why we’re evolving Fin’s pricing metric from resolutions to outcomes. This shift reflects how customers now define value: not just in full automation, but in safe, efficient progress toward the right result across complex, multi-step, and policy-constrained workflows.
An outcome represents when Fin successfully completes the action it was configured to perform, as part of a conversation. Resolutions are still one type of outcome Fin can deliver, where it handles the issue end-to-end. Another type of outcome can be a Procedure where Fin gathers context, takes action, and hands the conversation off when that’s what customers configured it to do.
Kick off your journey with the #1 Agent—an AI partner designed to turn resolutions into real outcomes. Tap “Start a free trial” to explore faster, smarter customer service and see how Fin delivers value from day one.
Increasing end-to-end AI resolutions is still a core component of scaling Agents, but they are no longer the only measure of Fin's success and utility. Especially as Fin takes on more complex work. Moving to outcomes recognizes that solving a customer problem with full automation isn’t always appropriate. It’s about getting to the right result, safely, and efficiently.
As Fin’s capabilities expand, teams should feel empowered to use it in more nuanced, collaborative work. Outcomes support that by allowing customers to design workflows that meet compliance requirements and include a human agent when necessary. From a product management standpoint, this is how we align incentives, keep risk controls intact, and still accelerate time-to-value.
Fin is becoming even more powerful at handling complex, multi-step support queries. With outcomes, we can support that growth without constantly reinventing how value is measured. And this change gives us a strong pricing foundation that can scale as Fin continues to grow and take on more roles beyond service. This aligns with our vision of Fin becoming a “Customer Agent,” capable of handling the entire customer experience.
What this means for pricing is intentionally straightforward. An outcome will be counted when Fin successfully completes an action it was configured to perform, as part of a conversation. That keeps the model predictable for finance leaders while staying transparent for operators and product teams managing AI workflows.
The pricing model stays simple and the definition of value becomes more accurate. In other words, we’re doubling down on fairness, predictability, and competitiveness—core tenets for any consumption SaaS pricing strategy tied to real business impact.
When we first wrote about outcome-based pricing, we said that trust is the currency of AI. That’s still true. Trust is earned when customers see pricing move in lockstep with utility and risk posture, especially as gen AI and agentic AI take on higher-stakes tasks.
Pricing has to feel fair, it has to be predictable, and it has to stay competitive. Evolving from resolutions to outcomes isn’t a departure from that belief. It’s the natural maturation of how we measure value as AI moves from simple Q&A into complex procedures and human-in-the-loop collaboration.
Fin has grown more powerful because customers asked more of it. Outcomes are how we reflect that progress honestly, while staying true to the same principles that guided us from the start. This is product strategy in action: align incentives, measure what matters, and scale what works.
And as Fin continues to get stronger, we’ll keep holding ourselves to the same standard: price based on the value delivered. That’s how we build durable trust, sustainable ROI, and a better customer experience at scale.
Building a great end-to-end customer experience with AI means going beyond support, and I’ve seen firsthand how transformative that shift can be when we treat every interaction as part of one cohesive journey.
Every customer touchpoint, from the first sales conversation through to post-sales support and success, is an opportunity to get it right. Other teams are now turning to AI to transform how they show up for customers, and support, which led the way, has already written the blueprint. In my role, I focus on making that blueprint actionable across the entire lifecycle.
In The 2026 Customer Service Transformation Report, it’s clear most businesses are thinking about what’s next, with more than half planning to scale AI to other departments. Interestingly, they often cite their early success with AI in support as motivation for the move. This makes support teams uniquely positioned to help lead the transition, a strategic role unimaginable just two years ago.
In this piece, I share how teams are introducing AI to other parts of the business, how to think about this expansion effort, and the new opportunities it creates for support leaders who want to drive a unified customer experience.
Support was the first proving ground for AI, and our research suggests that businesses are now planning to expand its use to other areas based on the results it’s yielded so far. Fifty-two percent of respondents said that their organizations are actively planning to scale AI to other departments in 2026.
What will this look like? Leading companies are already finding out.
Wins in support are setting the pace for company-wide AI. Survey results rank the drivers: proven success in support (57%), the push for a unified customer experience (49%), scaling other functions without more headcount (33%), and cross-department demand (31%).
My favorite example is WHOOP, the fitness wearables company. They offer a premium product which makes their sales conversations more consultative than transactional. Customers want to know “Which membership is right for me?” or “How often do I need to charge my WHOOP?” According to Emily Shirley, Business Manager for Growth Product at WHOOP, if someone chatted with the inside sales team, they were twice as likely to convert, but they didn’t have enough reps to respond to incoming queries fast enough. Customers could wait more than 10 hours for a reply.
With a big product launch on the line and an anticipated spike in prospective customer conversations, their three-person team needed help. So they deployed Fin to the "Join" page, the final step before purchase.
With Fin resolving 84% of inbound questions, the sales team was able to focus on high-value leads. Together, they drove a 130% increase in attributable sales. The team is now exploring ways to expand Fin beyond FAQs, focusing on personalised conversation flows, multi-product recommendations, and richer data capture. As Emily says: “There are so many parts of the buyer journey where this applies. We’ve only scratched the surface.”
It’s clear there’s a desire to push AI to other parts of the customer lifecycle, but there is a risk hidden in this expansion. If sales, customer success, and other departments all launch their own Agent, each operating in isolation, you can end up fragmenting the very thing our research says teams want to create. The second-most cited reason for pushing AI beyond support: desire for a unified customer experience.
Without shared context, each handoff becomes a source of friction where customers could receive inconsistent answers or be asked to repeat information. I’ve watched even well-intentioned AI rollouts struggle here—great local wins, but an overall journey that feels disjointed.
A translucent UI visual maps a support-led AI blueprint that scales across the business—from SDR and sales to custom assistants—anchored by layers for goals, memory and user context, business knowledge, and interoperability.
The opportunity (and the challenge) is to keep the customer at the center. Instead of department-specific Agents that operate independently, we must strive for cohesion. That means shared memory, consistent governance, and connected AI workflows that respect the customer’s history and intent across channels.
This is the future I’m building toward: solutions like Fin becoming a “Customer Agent,” capable of handling the entire customer experience. This will mean Fin can function in many roles, supported by a memory that grows with the customer over time and deep knowledge of the business, creating a seamless experience for every interaction. In practice, that’s agentic AI designed to collaborate across teams, systems, and journeys—without losing context.
Pushing AI into new parts of the business requires someone to own the process. And for many organizations, that’s the support team. Nearly a third of respondents (32%) confirmed their customer service teams are leading their business' AI transformation strategy.
This presents a real opportunity for support teams to shape the future of customer experience. Instead of each function reinventing the wheel, support can act as a center of excellence, defining shared standards, guardrails, and operating practices that drive performance.
“You already manage the most complex, high-volume customer interactions; you have rich data on customer needs and behavior; and you know how Agents perform in the real world. Those insights will be invaluable as AI scales across your business.”
Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.
In my organization, when we extended AI from support into sales, we deliberately brought our conversation design expertise, Agent Analytics, and governance models along with it. One team owns the orchestration, memory strategy, and CRM integration so a customer can start with a sales question and end up with a support one—without ever feeling a seam. That continuity is where journey mapping meets product strategy and turns into measurable outcomes.
As Agents like Fin expand their capabilities and move into new areas, I expect many customer service leaders will see their roles expand to include AI implementation across the customer journey. It’s a natural progression for product management leadership in support: owning the experience, the data, and the operating model.
Achieving perfect customer experience is AI’s biggest promise. But in order to get there, teams need to be smart about the solutions they deploy. A unified Customer Agent capable of handling the entire journey end-to-end will have a significant advantage, delivering consistent, context-aware experiences across every interaction.
The Customer Agent future is being built right now, and it’s starting with the team pioneering AI transformation from the very beginning: support. For leaders in these organizations, this is a rare opportunity to shape how customer relationships will be built and maintained in the AI era.
If you’d like to dig deeper into the data and benchmarks guiding these decisions, download The 2026 Customer Service Transformation Report.
"What if an AI could spot the moment two product teams start pulling in opposite directions — before it derails a quarter?" That question hooked me, because I’ve lived through the costly fallout of subtle misalignments that only surface at the end of a sprint—or worse, during quarterly business reviews.
I recently dug into an episode of Just Now Possible featuring Matthias and Charlotte Kleverud, co-founders of Momental. Their vision for "GitHub for product management" hits a nerve in the best possible way: find "merge conflicts" in strategy, not code, and do it early enough to save execution time, trust, and outcomes.
Here’s the core: Momental ingests documents, meeting transcripts, and voice recordings across an organization, then uses AI agents to map them into a structured context layer—a set of interconnected trees covering goals, decisions, learnings, and who's doing what. When it finds a conflict—say, one team betting on retention while another is prioritizing conversion—it surfaces the misalignment for humans to resolve, just like a merge conflict in code. That framing is both familiar (for anyone who’s shipped software) and powerful (for anyone who’s scaled product strategy across multiple teams).
Their journey tracks with what many of us have learned the hard way. "Starting in 2022 with DaVinci 002 and learning that the market wasn't ready for AI-assisted product thinking" pushed them toward experiments with agent teams. "The origin story: building a team of AI agents in 2024, only to discover agents hit the same alignment problems as humans" is exactly the kind of meta-lesson I’d expect when you scale autonomy without shared context. The breakthrough was an "OODA-loop-driven document processing agent" that continuously curates a living knowledge graph rather than relying on static prompts or brittle pipelines.
One model that stood out was "The product chain: signals → learnings → decisions → principles, and how AI maps it." That is the backbone of healthy product thinking. When this chain is explicit and inspectable, you can trace why a team chose Path A over Path B—and detect when new signals should invalidate old decisions. I’ve seen this accelerate continuous discovery and improve executive decision hygiene.
I also appreciated the organizational modeling: "Three trees that model an organization: the product tree (OKRs to epics), the wisdom tree (decisions and their reasoning), and the people/time tree." This maps cleanly to how we run quarterly planning at scale—tying outcomes to work, preserving rationale, and grounding ownership and timelines. With that structure, "How conflicts are detected, auto-resolved, or escalated to humans with merge options" becomes a pragmatic workflow, not a theoretical AI demo.
On the technical front, they’re blunt about limits: "Why traditional chunking and RAG breaks down at scale and what Momental does instead." Anyone who’s tried to stitch strategy from ad hoc notes knows that naive retrieval won’t cut it. You need durable context boundaries, rich metadata, and graph-aware reasoning. Which brings me to one of my non-negotiables: "Why metadata—who said it, when, and in what context—is critical to preventing hallucinations." In my world, we treat provenance like test coverage—you can’t ship without it.
Process-wise, the product philosophy resonated: "How a document processing agent uses OODA-loop thinking to extract and connect context across documents" reinforces the need for short feedback cycles, explicit hypotheses, and continuous refactoring of knowledge. Pair that with "The self-improving agent: collecting user feedback weekly and rewriting its own prompts" and you’ve got a blueprint for eval-driven development that keeps the system honest over time.
Their UI choices also mirror a pattern I’ve adopted: "Moving from chat-first to UI-first to proactive agents as an AI product design pattern." Chat can feel magical, but alignment work benefits from concrete artifacts—trees, timelines, driver trees, and opportunity solution trees—so people can reason together. Then, let proactive agents watch for drift and nudge teams before the cost of change spikes.
Two broader themes are worth calling out. First, "Specialized tools win" when the problem is deep, cross-functional context like product strategy. General-purpose chatbots struggle here; domain-specific models with strong information architecture have the edge. Second, product culture matters: "Discovery Versus Vibe Coding" is not just a catchy contrast—it’s a reminder that disciplined discovery beats intuition theater when stakes are high.
As for the roadmap, I’m encouraged by their "Design partner strategy and what's next for Momental's public launch." Early design partners are where you validate signal quality, precision of conflict detection, and the ergonomics of human-in-the-loop resolution. I’m especially curious how this intersects with LLMs for product managers, outcomes vs output OKRs, and product roadmapping and sprint planning in large portfolios.
Finally, a nod to the broader ecosystem. The conversation touched on "Claude Code" and a shift "Beyond documents and vectors" that many of us are living through—toward retrieval-first pipelines that respect context windows, stronger governance, and measurable improvements in decision quality. If you care about AI Strategy for empowered product teams, this is a space to watch—and to pilot.
Bottom line: If you’ve ever wished you could prevent strategy drift before it shows up in your dashboards, this "GitHub for product management" approach is worth your attention. Make the chain of signals, learnings, decisions, and principles explicit. Keep humans in the loop for the hard calls. And let proactive, agentic AI do what it does best: flag misalignments early, so your teams can move fast together.
Shipping agentic AI into production is exhilarating—until a flaky output torpedoes trust. Over the past year, I’ve led teams at HighLevel to operationalize agents across customer-facing and internal workflows, and I’ve learned that reliability isn’t an afterthought; it’s an architecture. In this piece, I share the AI Agent Orchestration Patterns for Reliable Products that consistently deliver dependable outcomes at scale.
When we talk about orchestration, we’re talking about more than a single prompt. The shift is from monolithic calls to coordinated “agentic AI” where routers, planners, and specialists collaborate through structured “AI workflows.” In practice, I rely on a few canonical patterns: a planner–executor loop for multi-step tasks, a router–specialist setup for skill selection, and a “retrieval-first pipeline” that grounds generation with authoritative context before a single token is produced.
Reliability-by-design starts with typed inputs/outputs and strict validation. I standardize on JSON schemas, enforce tool/function signatures, and implement idempotency keys so retries don’t wreak havoc on downstream systems. Timeouts, circuit breakers, and backpressure protect the platform under load, while rate limiting and dead-letter queues keep failure modes contained. Most importantly, we engineer graceful degradation: agents “abstain” when uncertain, fall back to deterministic paths, and escalate to humans instead of guessing.
Safety is a first-class concern, not a bolt-on. Our “AI risk management” pipeline includes PII redaction, allow/deny lists for tools and data, and the principle of least privilege for every connector (yes, even the ChatGPT connector). We codify policy-as-code for repeatability and require human-in-the-loop approvals for sensitive or irreversible actions. In my experience, clear red lines and reversible defaults prevent the vast majority of regrettable outcomes.
Without strong “observability,” you’re flying blind. I instrument agents with an “Agent Analytics” layer that captures traces, spans, tool invocations, and token usage across the entire chain. The essential metrics are outcome quality (task success rate), latency (p50/p95), tool failure rates, cost per task, and user-level satisfaction signals. Cross-agent lineage allows us to pinpoint where a plan went awry and which tool or prompt introduced drift—vital for rapid remediation.
Quality improves fastest when it is measured relentlessly. I practice “eval-driven development” with golden datasets, rubric-based scoring, and risk-weighted sampling of edge cases. LLM-as-judge can help, but we always calibrate against human ratings and monitor agreement. In production, I blend online metrics with controlled “A/B testing” and plan experiments to hit a realistic minimum detectable effect (MDE). The result is a virtuous loop where prompt tweaks, tool changes, and retrieval adjustments are verified before wide rollout.
Agents need the same rigor we expect from any modern system. I gate releases through “CI/CD” with linting for prompts, schema checks for tools, and simulation runs for critical paths. “Feature flags” enable shadow and canary deployments so we can throttle exposure by segment or workflow. I also track reliability with “DORA metrics” and “deployment frequency,” and I partner closely with “SRE” for on-call coverage, runbooks, and incident postmortems tailored to agent failure modes.
Context is a resource to allocate, not a bottomless pit. Thoughtful “context window management” means curating retrieval, summarizing long-running threads, setting memory time-to-live, and constraining what the agent can see at any given step. I bias hard toward retrieval over recall, keep chunks small and semantically precise, and validate that the “retrieval-first pipeline” truly returns the right evidence—not just the nearest match.
In day-to-day product work, I lean on a compact playbook: a router that selects the best specialist; a planner that decomposes tasks and allocates tools; a deterministic guard that verifies preconditions; an execution loop with explicit budgets; and a fallback policy that prefers abstaining over hallucinating. Together, these patterns create an agent that behaves like a dependable teammate rather than a creative wildcard.
No architecture thrives without the right rituals. Product trios keep discovery continuous, while clear outcomes (not output) align teams on value instead of vanity. We map risks early, maintain a public quality dashboard, and rehearse failure recoveries so incidents never become improvisations. The cultural signal is simple: we celebrate root-cause clarity and safe iteration over heroics.
If you’re just starting, implement three patterns first: retrieval before generation, abstain-and-escalate for low confidence, and canary releases under feature flags. Instrument everything from day one, run a weekly eval review, and expand scope only when the data says you’re ready. With these habits, your agents will earn user trust—and keep it.
To truly transform with AI, I’ve learned it’s never just about the technology—it’s about redesigning how we work. The teams that win don’t bolt AI on; they re-architect around it. That means rethinking roles, workflows, and governance to build a system that sustains and improves AI performance over time.
In The 2026 Customer Service Transformation Report, teams at every stage of maturity describe human agents taking on more proactive work—training AI systems, handling the hardest queries, and owning tasks that demand judgment. Job descriptions are shifting, too, with many organizations explicitly adding AI-related responsibilities.
I’m also seeing a clear rise in dedicated AI specialists. Conversation analysts, knowledge managers, and AI operations leads are fast becoming standard. For support professionals, this opens new, higher-leverage career paths—and creates a talent pipeline that blends service excellence, data fluency, and product thinking.
Support once centered on queue-level activity—ticket triage, routing, translations, and answering FAQs. Now, as AI handles more frontline interactions, our human roles are moving up the stack toward optimization, oversight, and continuous improvement.
According to the latest research, 45% of teams report updating job descriptions to include AI-related responsibilities, with 40% saying their human agents are now more focused on training AI systems. Another 27% report that human agents primarily handle the most complex escalations and edge cases, while a quarter say agents are doing more consultative and strategic work.
Even at the initial deployment stage, 16% of teams report spending less time handling support volume since implementing AI – and among teams who’ve reached maturity, that figure rises to 28%.
When Intercom’s Research, Analytics & Data Science (RAD) team interviewed 166 of our customers, similar themes emerged. Nearly all participants (≈95%) reported meaningful workflow changes, with manual processes being handled by AI, and humans focusing more on monitoring or fine-tuning AI outputs. Eighty-three percent of participants also reported seeing their team’s roles and responsibilities change to become more strategic and supervisory in nature.
AI is reshaping support teams: organizations are adding conversation analysts (32%), knowledge managers (30%), AI operations leads (28%), and support automation specialists (24%). Just 8% report no new AI roles.
It’s not just the work that’s evolving; organizational structures are, too. Some teams are reallocating existing talent into AI-focused roles; others are hiring entirely new skill sets. Many of the most common job titles in this space didn’t exist two years ago.
Consider a Senior AI Knowledge Manager, Beth-Ann Sher, who transitioned from a help center manager role. Like many careers transformed by AI, her work evolved from administrative to strategic. Instead of focusing solely on customer-facing, self-serve content, her mandate expanded to designing and optimizing knowledge inputs that directly improve AI Agent Fin’s performance—work that materially lifts resolution rates.
Or look at a Senior Conversation Designer, Fred Walton, hired specifically for an AI-first function. He focuses on frictionless customer journeys with Fin, smoothing handoffs between automation and human support while keeping customer satisfaction front and center—hallmarks of mature AI workflows and conversation design.
In high-performing organizations, roles like these typically sit within a dedicated AI support team under senior CS leadership. Clear ownership and accountability for AI performance is critical; without it, optimization stalls and trust erodes.
These shifts aren’t isolated. Take Robb Clarke from RB2B. He went from Head of Technical Operations to Head of AI. With Fin, his focus moved from repetitive support questions to managing knowledge and improving the system behind it—freeing him to be proactive about product improvements and fix issues before they hit customers.
Or consider Eric Broulette from Bloomerang, a support leader who leaned into AI and became the VP of Support and Education. By deploying Fin, his team found breathing room to invest in what’s next. Agents stepped into new roles, contributed to meaningful projects, and built skills that had previously felt out of reach. As Eric puts it: “Do not wait to embrace AI. It will unlock more career growth for your teams than you can imagine.”
Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.
Bringing AI into support will eventually change every agent’s day-to-day work. For leaders at the start of the journey, that can feel daunting. My perspective: the most successful teams treat this as an operating model shift, not a tooling rollout—anchored in AI Strategy, governance, and continuous improvement.
Be transparent about what’s changing, why it matters, and how success will be measured. Define how AI performance will be evaluated (resolution rate, containment, CSAT impact), empower agents to train and improve the system, and communicate how responsibilities will evolve. When teams help build the AI, they’re invested in making it great.
Here’s the playbook I rely on with support leaders: First, reset expectations about time allocation—less time in the queue, more time improving the AI system that serves the queue. Second, elevate knowledge management as a core capability. Prioritize content quality and coverage for your AI Agent, and carve out dedicated “out of the inbox” time so every agent contributes. Third, keep outcome metrics—especially resolution rate—front and center. It gives the team a north star for experimentation and iteration.
Scaling AI is as much a people challenge as it is a technology challenge. As automation takes on more work, support roles become more proactive, strategic, and cross-functional—even early in the journey. Responsibilities expand, new roles emerge, and team structures adapt to concentrate on and amplify AI performance. In the process, support careers are transformed.
If you’re leading this shift, now’s the moment to reimagine your operating model: clarify ownership, invest in knowledge and conversation design, adopt eval-driven development, and build the muscle for continuous improvement. That’s how you move from tickets to strategy—and unlock compounding value for your customers, your business, and your teams.
I’ve led product organizations through multiple growth chapters, and the pattern is always the same: the tighter the alignment between product, sales, and marketing, the faster you scale. Reflecting on the journey of Chris Degnan — the first sales hire at Snowflake who spent 11 years helping scale the company from zero to $3.5 billion in revenue as its CRO while partnering with four different CEOs — I’m struck by how consistently the fundamentals win. The playbook isn’t mysterious; it’s disciplined execution, ruthless clarity, and a go-to-market strategy that matures with each revenue stage.
At $10M ARR, the CRO role is hands-on and founder-adjacent. You’re close to the product, running point on key deals, pressure-testing messaging, and building credibility with early customers. By $1B+, the job is organization design: segmentation, international expansion, forecast accuracy, enablement, recruiting, and cross-functional orchestration. The shift is from deal quarterback to system architect — standing up repeatable, auditable processes that produce reliable outcomes across regions, segments, and industries.
Sales leaders who can’t sell the product themselves don’t last. Whether you sit in product management leadership or run the field, you need to master discovery, speak the customer’s language, and translate use cases into value. That also means getting fluent in solutions engineering — understanding integrations, data paths, security, and the operational realities buyers live with. I’ve found this hands-on competence to be the fastest way to earn trust internally and externally, and to keep product strategy grounded in market truth.
The MEDDIC methodology is the foundation for every durable sales org — and, frankly, a founder’s best insurance policy. MEDDIC forces alignment on qualification criteria, from Metrics to Economic Buyer to Decision Process and Identifying Pain. When product and sales both operate to this standard, roadmap bets improve, marketing targets sharpen, and win rates climb. It’s not paperwork; it’s pattern recognition at scale.
High-output CROs obsess over the right numbers. Pipeline coverage by segment and stage; conversion rates through each gate; sales cycle length by use case; average selling price and discount discipline; consumption predictability when you have consumption SaaS pricing; and post-sale expansion velocity. The art is deciding which two or three metrics are the organization’s true north at a given stage — then designing enablement, compensation, and operating cadence around them.
On operating cadence, the week in the life at scale is predictable for a reason. Forecast reviews that surface risk early. Deal reviews that coach to MEDDIC depth, not activity theater. Enablement blocks to uplevel managers and ICs. Recruiting time — always. Customer roadshows to refine value proposition and product positioning. And standing meetings with product, marketing, and finance to keep the GTM motion, roadmap, and unit economics in sync.
Compensation is a force multiplier or a silent saboteur. Keep it simple, consistent, and aligned to the current motion. Early on, weight new logo acquisition and land quality; as you mature, balance new business with expansion, multi-product adoption, and healthy consumption. Guardrails matter — cap over-discounting, reward multi-threading, and avoid plans that create end-of-quarter cliff behavior. The best plans reinforce the behaviors you want your culture to scale.
Technical CEOs often underestimate how much narrative, segmentation, and process discipline great GTM requires. The handoff from founder-led GTM to sales-led growth is where many teams stall. My rule: prove one repeatable motion in one segment before you add complexity. Codify the buyer’s journey, instrument the funnel, and make sure product strategy and enablement move in lockstep.
Culture sets the ceiling. You have to find the fakers, manage-uppers, and passengers quickly — people who look busy but don’t move pipeline, who talk big but avoid accountability, or who ride the momentum of others. The mantra that has saved me endless time: “When there’s doubt, there’s no doubt”. Move fast, but with humanity; be clear on expectations, coach hard, and when it’s not a fit, make the change before the team does it for you.
Feedback is the operating system of a high-performing org. Leaders at every level need to be coachable — on message discipline, on forecast rigor, on how they develop people. I’ve benefited from straight talkers who hold a high bar, and I try to pay that forward. The fastest way to raise organizational IQ is to institutionalize feedback loops across sales, product, and marketing — from post-mortems to win-loss analysis to field-sourced roadmap reviews.
What separates exceptional ICs from the rest? Hunger, intellectual honesty, and a builder’s mindset. They qualify hard, align to customer metrics early, multi-thread to power and value, and partner tightly with solutions engineering. They don’t hide from gaps; they surface them, and they know exactly what they need from product, marketing, and leadership to win.
Executive teams that scale share a few traits: crisp segmentation decisions, single-threaded ownership for outcomes, and healthy conflict that resolves into commitment. Dysfunction, by contrast, looks like metrics roulette, opaque decision-making, and a tolerance for exceptions that become precedent. Make the rules explicit and the exceptions rare.
Leaders like Frank Slootman have popularized intensity, speed, and focus — and there’s real power there when paired with clarity and data. The lesson I carry forward: move fast on people decisions, keep the message simple, and measure what matters. Equally important is knowing where that approach can backfire — when speed outruns learning, or when pressure erodes cross-functional trust. The best operators balance urgency with systems thinking.
Most AI companies will face a go-to-market reckoning. Model quality won’t save a weak motion. The winners will articulate a hard-nosed ROI, solve specific workflow pain, address data governance and security head-on, and show measurable lift — not demo dazzle. In other words, the same fundamentals apply; the stakes and scrutiny are just higher.
If you’re building or rebuilding your revenue engine, start here: define your ideal customer profile and segmentation with ruthless clarity; adopt MEDDIC and teach it across product and sales; align compensation to today’s motion; instrument the funnel and inspect it weekly; and cultivate a culture where feedback is fuel. Do that, and the path from $0 to $3.5B stops feeling like mythology — and starts looking like math.
Human-in-the-loop oversight is the fastest and most reliable way I know to elevate AI quality, build user trust, and reduce risk. At HighLevel, my teams treat oversight as a product feature—not an afterthought—because dependable AI experiences come from deliberate design choices across data, models, and people.
When I say “human-in-the-loop,” I mean a system that blends automation with targeted human judgment at key moments: during data curation, prompt engineering, evaluation, deployment, and post-launch learning. This approach turns “AI workflows” into measurable, repeatable processes and keeps me honest about what’s working, what’s drifting, and where a human safety net must step in.
Architecturally, I start with a retrieval-first pipeline to ground outputs in trusted knowledge, then wrap it in guardrails. Deterministic preprocessing, careful prompt engineering, and post-processing validators catch obvious failure modes. Confidence thresholds and policy checks route ambiguous or sensitive cases to a human reviewer, while clear, auditable traces show why the system chose automation versus escalation. This balance supports reliability at scale while preserving agility for “agentic AI” patterns when they add value.
Quality is only real if I can measure it, so I build with eval-driven development from day one. I maintain golden datasets, rubric-based scoring guidelines, and an automated evaluation harness that runs on every change to prompts, models, or data. Pre-production gates protect against regressions, while production telemetry surfaces drift by segment and use case. When it’s time to run experiments, I use A/B tests sized with a minimum detectable effect (MDE) to avoid overfitting to noise.
Operationally, I optimize for outcomes, not output. I track task success rate, time-to-resolution, safety violation rate, hallucination rate, and cost-to-serve, then connect these to outcomes vs output OKRs. The signal I want is simple: are we reliably solving the user’s job-to-be-done with lower effort and higher confidence? If not, I tighten prompts, refine retrieval, or expand human review where it pays off most.
Risk governance is non-negotiable. I design with privacy-by-design and data governance from the start—role-based access, audit trails, PII redaction, and red-team tests for safety. Clear reviewer playbooks and calibration sessions reduce bias and ensure consistent decisions. These practices aren’t bureaucracy; they’re how I operationalize AI risk management while maintaining velocity.
Teams make or break this model. I empower product trios to own the full lifecycle—discovery, build, and learning—so feedback loops close quickly. In-product feedback widgets, reviewer queues, and incident management playbooks help us respond in hours, not weeks. Over time, human review becomes a targeted scalpel rather than a blanket requirement as the system learns and improves.
Economics guide the level of oversight. I treat each workflow like a portfolio: where the value of accuracy is high and ambiguity is common, I route more to humans; where tasks are simple, frequent, and well-bounded, I automate aggressively. The goal isn’t zero humans—it’s optimal humans, deployed precisely where their judgment compounds ROI.
If you’re getting started, begin with one high-impact workflow, establish your golden set and evaluation rubric, and wire in a simple review queue. Prove the lift, then scale. In the short video above, I walk through the patterns I use to design these loops, measure quality with rigor, and ship AI that teams—and customers—can trust.
I ask one question before I green‑light any new AI feature: is our analytics truly AI‑ready? If the answer is no, we slow down, because nothing derails an AI roadmap faster than shipping features we can’t measure, iterate, or trust. Over time, I’ve learned that the right analytics foundation is the difference between a flashy demo and a durable, compounding product advantage.
"Product and engineering teams face new challenges when building AI-first products. A modern digital analytics platform offers solutions." I agree—and I’d add that the real win comes when model metrics and product outcomes live in one coherent system, so we can connect every improvement to customer value.
Here’s what “AI‑ready” analytics means in practice for me: a unified event taxonomy tied to clear user and account identities; consistent product analytics (activation, funnels, retention analysis, cohorts); ground‑truth labels and feedback signals for model evaluation; and a single source of truth that blends model telemetry with user behavior. When those pieces click, our AI Strategy turns from guessing to “eval‑driven development.”
Start with data governance and privacy‑by‑design. Define event names, properties, and versioning rules up front. Capture the context that AI needs—inputs, outputs, confidence scores, content types—without storing unnecessary PII. This discipline reduces rework, improves observability, and keeps auditors and customers confident in how we handle data.
Next, operationalize eval‑driven development. I run offline evaluations with representative datasets, then shadow mode in production, and finally controlled rollouts with A/B testing and feature flags. We set a minimum detectable effect so experiments are conclusive, and we include AI risk management metrics—like safety violations, fallback rates, and moderation triggers—alongside core product KPIs such as activation, task success, and time‑to‑value.
On the product analytics side, I rely on a unified analytics platform (e.g., Amplitude analytics or similar) to track adoption of AI features: who sees the feature, who tries it, who repeats it, and who retains because of it. Cohort analyses help me isolate lift among target segments; CRM integration connects usage to revenue; and pathing highlights where users need guidance. This is the engine of product‑led growth for AI capabilities.
Quality and observability complete the loop. I monitor latency, error rates, and cost per successful outcome, but I also watch human‑grounded proxies: thumbs up/down, edits after AI suggestions, and deflection and CSAT for support workflows. These signals feed back into prompt engineering, retrieval quality, and model selection—closing the gap between LLM behavior and customer value.
None of this works without strong cross‑functional rituals. Product trios align on success metrics before we write a line of code; continuous discovery validates user problems; and QBRs versus OKRs are reconciled so we invest in durable capabilities, not just quarterly spikes. When analytics and discovery move in lockstep, we ship fewer speculative features and more compounding improvements.
Finally, choose build versus buy intentionally. I buy a robust, scalable analytics substrate and only build the custom AI evals I need for proprietary use cases. With feature flags in CI/CD and automated schema checks, instrumentation becomes part of deployment frequency—not an afterthought. The result is a reliable runway to scale AI‑first products without losing speed, safety, or clarity.
If you want a quick readiness check: do you have a clean event schema, identity resolution, and governed properties; a measurable definition of activation for each AI feature; offline and online evals connected to business KPIs; guardrails and human feedback in the loop; and dashboards that team leaders actually use? If not, start there. The payoff is faster iteration, lower risk, and a clearer line from AI investment to customer outcomes.
Inspired by this post on Amplitude – Perspectives.
For years, I chased the elusive goal of delivering a perfect customer experience. Today, with AI embedded in our support operations, that standard is finally within reach—and it’s reshaping how we prioritize, design, and scale service.
In “The 2026 Customer Service Transformation Report,” teams report early, tangible wins from AI: faster responses, higher efficiency, and consistent coverage across languages and time zones. Those gains create the capacity we’ve always needed. The more we push the technology, the more quality improvements we unlock.
This marks a fundamental shift. As AI takes on more, our focus can finally move from firefighting to crafting the customer experience. When the AI is working, the measure of success becomes how well it’s working—across accuracy, tone, resolution, and end-to-end journey quality.
I’ve seen this transformation firsthand. Mature AI deployment gives my team “breathing room,” so we can design for consistently excellent outcomes rather than obsess over deflection. That means widening access to support, removing friction on the path to resolution, and anticipating customer needs before they escalate.
In our own support organization, we opened support to trial customers, accelerated first response times, and added consultative sessions during onboarding. We absorbed a 300% increase in total demand without adding headcount—made possible by deep integration of an AI Agent and a disciplined AI strategy.
Teams with mature customer service deployments are nearly three times likelier to say they always meet increasing expectations—27% vs 9% at initial rollout—highlighted by bold orange and gray comparison bubbles.
Across the industry, the pattern is similar. When teams initially deploy AI, only 9% say they can always meet customer expectations. That number triples as teams reach a mature level of deployment. Even as expectations rise, the organizations that deeply integrate AI—complete with clear ownership, robust instrumentation, and continuous improvement loops—are the ones most likely to meet (and exceed) the bar.
Looking ahead to 2026, I expect omnichannel consistency to become a key differentiator. The data shows planned investment is distributed nearly equally across chat, email, and social messaging (36% each), closely followed by phone/voice (31%). The question is no longer “Which channel should we optimize?” but “How do we deliver a consistent, AI-powered experience everywhere our customers are?”
Teams that solve for omnichannel consistency will bridge the long-standing gap between what customers expect and what support can deliver. Every touchpoint becomes an opportunity to exceed expectations and build durable trust.
Consider Clay, a team that scaled support without sacrificing quality. Support is one of their main growth drivers, and as their customer base expanded, ticket volume surged. Early on, they concentrated much of their effort in Slack, cultivating close, transparent community relationships. But relying on a single channel created friction as they grew; customers wanted the flexibility of email and in-app chat, and Clay needed to deliver the same high standard everywhere.
Where AI investment is headed for customer service in 2026: chat, social, and email lead at 36%, with phone/voice close behind at 31%. A bold visual snapshot of shifting channel priorities in CX.
By unifying their support experience with an AI Agent, Clay brought consistency across channels. Today, AI is involved in 90% of all queries and handles half of Clay’s total volume, upwards of 7,000 queries a month. First response rates improved significantly, freeing the team to focus on proactive, high-impact work.
That work includes identifying content gaps for education and content marketing, reaching customers before they need to ask for help, and surfacing feature requests and recurring challenges to product teams. Clay proves that when support is truly great, it becomes a competitive edge.
So how do you build a superior customer experience with an AI Agent? Here are five principles I use when scaling toward mature deployment.
1) Treat customer experience like a product. Treating support as a product means designing, building, and managing the support experience with the same rigor as your core product. You define goals (faster onboarding, higher CSAT or CX Score, lower churn). You map flows (AI starts the conversation, human handovers, proactive nudges). You instrument the journey (track handoffs, drop-offs, success states). You run tests and ship improvements (tone tweaks, fallback paths, training updates). You own the outcomes (gather feedback, measure performance, use insights to continuously improve the system).
Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.
2) Lead with AI, back with humans. AI isn’t replacing the human touch. It’s redefining when, where, and how it’s most valuable. In a scaled model, AI is the first responder and the end point for most conversations. Humans step in where they add the most value—particularly during high-stakes issues—and those handoffs should feel seamless. Meanwhile, your team focuses on improving AI performance and optimizing the end-to-end journey.
3) Be proactive. Use AI to anticipate needs, guide customers before problems arise, and nudge them toward successful outcomes. This is where customer support AI strategy shines—moving from reactive triage to journey orchestration that protects momentum and builds trust.
4) Build for trust. Many customers still carry the legacy of clunky chatbots that delivered vague answers and dead ends. You earn trust by showing that your system works. Don’t hide your AI Agent behind layers of “choose an option.” Get customers to the AI quickly, demonstrate real problem-solving, and ensure that when a human is needed, they join with full context to resolve complex issues efficiently.
5) Make it feel personal. Your AI Agent represents your brand. The way it speaks, follows policies, and responds matters. Use tone control, fallback logic, and language preferences to align the experience to your standards. Consistency builds trust; personality builds connection and loyalty.
Perfect really is possible. With deep AI implementation, you can scale comprehensive, fast, and personal support across channels—so customers feel supported not just when they reach out, but throughout their journey. That’s the promise of modern AI workflows in support, and it’s what will separate leaders from laggards in the years ahead.
